Track accepted paper

CiteScore: 0.97ℹ
CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor: 0.698ℹImpact Factor:2016: 0.698The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

5-Year Impact Factor: 0.815ℹFive-Year Impact Factor:2016: 0.815To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

Source Normalized Impact per Paper (SNIP): 1.006ℹSource Normalized Impact per Paper (SNIP):2016: 1.006SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR): 0.569ℹSCImago Journal Rank (SJR):2016: 0.569SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Author StatsℹAuthor Stats:Publishing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in Scopus.

Natural computation focuses on nature-inspired algorithms, such as neural networks, genetic algorithms, molecular computing, quantum computing, and swarm optimization, which have enjoyed numerous applications in a wide range of complex problems in science and engineering. The problems tackled can be broadly categorized into global/multi-objective optimization, classification, or regression. Recent break-through in deep learning and big data has greatly amplified this trend.

Money or data tansfers, contacts between individuals, product sales, network traffic, messages, or travels may all be modeled as link streams, i.e. sequences of links with temporal information. Studying the structure and dynamics of such streams is therefore crucial for many fundamental and applied questions. This raises many challenging issues, which are at the core of an intense research activity currently, with contributions from graph theory, combinatorics, probabilities, complex networks, signal processing, and others.

Graph structures are used to model computer networks. Servers, hosts or hubs in a network represent vertices in a graph and edges represent connections between them. Each vertex in a graph is a possible location for an intruder (fault in a computer network, spoiled device) and, in this sense, a correct surveillance of each vertex of the graph to control such a possible intruder is worthwhile. According to these facts, it is desirable to uniquely recognize each vertex of the graph. In connection with this problem, the notion of metric generators (also called resolving sets or locating sets) were introduced in the 1970's and, due to this, the concept of metric dimension in graphs is nowadays well studied, which is also somehow based on the fact that the number of researchers on the topic have significantly increased in the last two decades.

We invite submissions of papers on the theory and practice of formal methods for computational systems biology and design of molecular devices for publication in a special issue of the Journal of Theoretical Computer Science (TCS), Section C (Theory of Natural Computing).